1. Prajwal Panchmahalkar Nishanth Reddy Kommidi

2.  Wireless sensor networks are type of MANET’s
 These networks monitor physical or environmental conditions, such as
temperature, sound, vibration using the environmental energy supply.
 Applications
 Environment monitoring
 Surveillance
 Military
 Emergency conditions
 Health application

3. Environment monitoring
Sports and robotics

4. Healthcare
Military
Surveillance

5.  This paper proposes a geographic routing with environmental energy supply
using two protocols GREES-L, GREES-M
 This protocol combines geographic routing and energy efficient routing
techniques.
 Considers lossy wireless channel conditions and renewal capability of
environmental energy supply .
 Energy constraints are the most crucial considerations of wireless sensing
networks
 The objective of this paper is minimizing the energy consumption and
maximizing the network lifetime.

6.  This technique uses routing algorithms which maximize network lifetime
and maximize the total number of successfully delivered messages.
 These techniques assume that the nodes have a limited or fixed energy
supply.
 Drawback
 Did not take into account the node’s capability of extracting energy from the environment.

7.  This technique the sender node needs to know the location of itself, one-
hop neighbors, and destination.
E
A C
G
B F
D
 C needs to know its immediate neighbors to forward to E
 Geographic forwarding needs a location service!

8.  Energy harvesting is done to improve the lifetime and performance in the
wireless sensor networks.
 Energy Sources

9.  Environmental energy is a continued supply of energy which will allow the system to last
forever.
 There is an uncertainty associated with its availability and measurement , compared to the
energy stored in the battery.

10.  Routing protocols that efficiently direct packets along low cost links.
 Balance residual energy on the nodes with environmental energy supply
 GREES routing techniques combine the progressive packet advancement
towards the destination considering both the power aware and geographical
routing techniques utilizing the environmental energy supply.

11. Harvest Rate: 2 units/sec
Consumption rate:
2units/relay
B Eb -4
A
D
C
Harvest Rate: 1 units/sec
Consumption rate:
Eb -2 2units/relay
Initial Battery Status before the Transmission at node B and
node C

12. Harvest Rate: 2 units/sec
Consumption rate:
2units/relay
B Eb
(Eb – 4) + (5x2) = Eb
A
D
(Eb – 2) + (5x1) – (5x2) = Eb - 7
C
Harvest Rate: 1 units/sec
Consumption rate:
Eb - 7 2units/relay
Node A transmits 5 packets to C as C has more residual
energy

13. Harvest Rate: 2 units/sec
Consumption rate:
2units/relay
B Eb
A
D
C
Harvest Rate: 1 units/sec
Consumption rate:
Eb - 7 2units/relay
Now node B has more residual Energy than B so A sends packets to B in
next hop

14.  Frame Delivery ratio (FDR)
 For example consider Frame Delivery ratio from node i to node j FDRij ,
◦ Periodic Time based event (T)
◦ When j receives “Hello” packet (H)
 Exponentially weighted moving average (EWMA) is used to calculate Link
Quality Estimation.

15. “H” even
occurs when j
recieves a
“hello”
packet.
“T” event
occurs at
regular
time
intervals

16. Nm -> known misses
Currentseg -> seq. no of current
packet
Lastseg -> seq. no of last received
packet
lastHello -> time of the last hello
packet received
L -> misses fed into the estimation
algorithm
FDR -> Frame Delivery Ratio
ϒ -> tunable parameter
Ʈ -> Frequency of transmission
Ng->Guess on the number of
missed packets

17.  This technique allows j to measure FDRij and i to measure FDRji
 Each hello message sent by i contains the FDR measured by i from each of
its neighbors.
 Each neighbor then gets a FDR to i whenever it receives a probe from i.
 This FDR helps us to estimate the link quality and the efficiency of
network.

18.  The cost for a node to send or receive data is a linear function
 Proportional to size of the packet
 Cost = C x Spkt + b

19.  Energy harvested through sun light, air etc.,
 High uncertainty and not homogenous at all nodes
 Mean of harvesting µi is considered with energy harvesting varying
between Pimin and Pimax
Note: Energy storage reservoirs can be used.

20.  GREES-L is a linear function that combines linear geographical advance
efficiency and the energy availability.
 Its considers packet advancement towards the destination along with link
quality estimation and the energy achievable at the node.

21. Ni
i D

22. CL(Ni,D) = 1 / (α.NPRO(i, Ni, D) + (1 – α).NE(Ni)
Ni
CL -> Cost when node i transmits the packet to the neighbor Ni
towards the destination D
NPRO->Normalized progressive distance per data frame from i to Ni
NPRO(i, Ni, D) = PRO(i, Ni, D) / Max{PRO(i, Ni, D) }
Where,
PRO(i, Ni, D) = (dist(i, D) - dist(Ni, D)). FDRiNi. FDRNii

23.  NE(Ni) is normalized effective energy on node Ni
 NE(Ni) = E(Ni)/ Max{E(Ni)}
 E(Ni) = β.(µNi - ΨNi).(tc – tl ) + Er (Ni)
 µNi last received expected energy harvesting rate of Node Ni
 ΨNi last received expected energy consuming rate of Node Ni
 tc time when node i is forwarding the packet
 tl time when last broadcasting hello message from node Ni is heard by i

24.  To reduce the cost function (CL) - maximize the denominator
 It can be divided into two parts
◦ Progressive packet advancement towards destination
◦ Estimated energy availability
 If all the nodes have same energy harvesting rate and residual energy node
I will transmit the packets to the neighbors with larger PRO towards the
destination
 If α = 1 GREES-L degrades to Geographic routing
 If α = β = 1 GREES- L degrades to Energy Aware only routing

25.  GREES-M uses multiplication to balance the geographical advance
efficiency per packet transmission and the energy availability on receiving
nodes.
 Cost Function
CM(Ni,D) = (Eb(Ni). ηλNi)/(logŋ.(μNi + ϵ).PRO(i,Ni,D))
◦ Where λNi = Eb(Ni)- Er(Ni)/ Eb(Ni) {λNi= Fraction of energy used at
node Ni}
 The cost function here is an inverse function of energy harvesting rate and
the geographical advancement towards the destination
 Also an exponential function of nodal residual energy.
 Note: Eb(Ni) in numerator doesn’t mean nodes with higher capacity have
higher cost, because Eb(Ni) is embedded in ηλNi which is a cost metric

26.  GREES-L and GREES-M are the two energy aware geographic routing
protocols which consider both the wireless lossy channels condition and
energy constraints of the network, which were not taken into consideration
by previous traditional techniques. Previous techniques considered either
the network lifetime or in maximizing the successful delivery messages.
Most of the energy to the network is through the environment hence, a lot
of battery energy is conversed.
 Also we suggest that a mobile charging station can be used which can
distribute the harvested energy among the hosts where a uncertainty in
energy harvesting and consumption is observed